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      Chromatin environment, transcriptional regulation, and splicing distinguish lincRNAs and mRNAs

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          Abstract

          While long intergenic noncoding RNAs (lincRNAs) and mRNAs share similar biogenesis pathways, these transcript classes differ in many regards. LincRNAs are less evolutionarily conserved, less abundant, and more tissue-specific, suggesting that their pre- and post-transcriptional regulation is different from that of mRNAs. Here, we perform an in-depth characterization of the features that contribute to lincRNA regulation in multiple human cell lines. We find that lincRNA promoters are depleted of transcription factor (TF) binding sites, yet enriched for some specific factors such as GATA and FOS relative to mRNA promoters. Surprisingly, we find that H3K9me3—a histone modification typically associated with transcriptional repression—is more enriched at the promoters of active lincRNA loci than at those of active mRNAs. Moreover, H3K9me3-marked lincRNA genes are more tissue-specific. The most discriminant differences between lincRNAs and mRNAs involve splicing. LincRNAs are less efficiently spliced, which cannot be explained by differences in U1 binding or the density of exonic splicing enhancers but may be partially attributed to lower U2AF65 binding and weaker splicing-related motifs. Conversely, the stability of lincRNAs and mRNAs is similar, differing only with regard to the location of stabilizing protein binding sites. Finally, we find that certain transcriptional properties are correlated with higher evolutionary conservation in both DNA and RNA motifs and are enriched in lincRNAs that have been functionally characterized.

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          Most cited references54

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          BEDTools: a flexible suite of utilities for comparing genomic features

          Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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            TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions

            TopHat is a popular spliced aligner for RNA-sequence (RNA-seq) experiments. In this paper, we describe TopHat2, which incorporates many significant enhancements to TopHat. TopHat2 can align reads of various lengths produced by the latest sequencing technologies, while allowing for variable-length indels with respect to the reference genome. In addition to de novo spliced alignment, TopHat2 can align reads across fusion breaks, which can occur after genomic translocations. TopHat2 combines the ability to identify novel splice sites with direct mapping to known transcripts, producing sensitive and accurate alignments, even for highly repetitive genomes or in the presence of pseudogenes. TopHat2 is available at http://ccb.jhu.edu/software/tophat.
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              Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.

              Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.
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                Author and article information

                Journal
                Genome Res
                Genome Res
                genome
                genome
                GENOME
                Genome Research
                Cold Spring Harbor Laboratory Press
                1088-9051
                1549-5469
                January 2017
                January 2017
                : 27
                : 1
                : 27-37
                Affiliations
                [1 ]Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts 02138, USA;
                [2 ]Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts 02142, USA;
                [3 ]Department of Biological and Biomedical Sciences, Harvard University, Boston, Massachusetts 02115, USA;
                [4 ]Department of Pathology, Beth Israel Deaconess Medical Center, Boston, Massachusetts 02215, USA
                Author notes
                Corresponding author: john_rinn@ 123456harvard.edu
                Article
                9509184
                10.1101/gr.214205.116
                5204342
                27927715
                4cb17f27-b787-4fc8-9822-624d15e38d38
                © 2017 Melé et al. Published by Cold Spring Harbor Laboratory Press

                This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 8 April 2016
                : 9 November 2016
                Page count
                Pages: 11
                Funding
                Funded by: US National Institutes of Health http://dx.doi.org/10.13039/100000002
                Award ID: R01 ES020260
                Award ID: R01 MH102416
                Award ID: P01 GM099117
                Funded by: National Science Foundation http://dx.doi.org/10.13039/100000001
                Award ID: DGE1144152
                Categories
                Research

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